Machine learning on brain MRI data for differential diagnosis of Parkinson’s disease and progressive supranuclear palsy
Abstract
Background Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with clinically diagnosed Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP).Method Morphological T1-weighted Magnetic Resonance Images (MRIs) of PD patients (28), PSP patients (28) and healthy control subjects (28) were used by a supervised machine learning algorithm based on the combination of Principal Components Analysis as feature extraction technique and on Support Vector Machines as classification algorithm. The algorithm was able to obtain voxel-based morphological biomarkers of PD and PSP.Results The algorithm allowed individual diagnosis of PD versus controls, PSP versus controls and PSP versus PD with an Accuracy, Specificity and Sensitivity>90%. Voxels influencing classification between PD and PSP patients involved midbrain, pons, corpus callosum and thalamus, four critical regions known to be strongly involved in the pathophysiological mechanisms of PSP.Comparison with existing methods Classification accuracy of individual PSP patients was consistent with previous manual morphological metrics and with other supervised machine learning application to MRI data, whereas accuracy in the detection of individual PD patients was significantly higher with our classification method.Conclusions The algorithm provides excellent discrimination of PD patients from PSP patients at an individual level, thus encouraging the application of computer-based diagnosis in clinical practice.
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Within-subject variability is usually excluded in currently available research.
“Within-subject variability is usually excluded (Finn et al., 2015; Salvatore et al., 2014).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Salvatore et al., 2014
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- Authors
- Salvatore, C., Cerasa, A., Castiglioni, I., Gallivanone, F., Augimeri, A., Lopez, M., Arabia, G., Morelli, M., Gilardi, M. C., & Quattrone, A.
- Affiliations
- University of Milano-Bicocca (IT)
- Institute of Neurological Sciences (IT)
- Institute of Molecular Bioimaging and Physiology (IT)
- Magna Graecia University (IT)
- Corresponding author
- Castiglioni, I.
- ORCID
- Venue
- Journal of Neuroscience Methods
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 222
- Pages
- 230-237
- ISSN
- 0165-0270 (ISSN-L)
- OpenAlex
- W2037984755
- Cited by
- 299
- References
- 38
- Citations by year
- 2026: 15 · 2025: 26 · 2024: 25 · 2023: 36 · 2022: 41 · 2021: 24 · 2020: 37 · 2019: 23 · 2018: 27 · 2017: 24 · 2016: 5 · 2015: 15 · 2014: 1
- FWCI
- 4.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Neuroscience Methods (publishedVersion)
- PubMed (publishedVersion)
- BOA (University of Milano-Bicocca) (submittedVersion)
- Topics
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Parkinson's disease, progressive supranuclear palsy, differential diagnosis, brain MRI, voxel-based morphometry, computer-aided diagnosis, T1-weighted imaging, support vector machine, principal component analysis, midbrain, thalamus, passive optical networks, corpus callosum
- MeSH terms
- Aged, Algorithms, Artificial Intelligence, Brain, Corpus Callosum, Diagnosis, Differential, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Mesencephalon, Parkinson Disease, Pons, Retrospective Studies, Sensitivity and Specificity, Supranuclear Palsy, Progressive, Thalamus, Principal Component Analysis, Support Vector Machine
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